Cloud-based machine learning system and data fusion for the prediction and detection of corrosion under insulation

ABSTRACT

A system for predicting corrosion under insulation (CUI) in an infrastructure asset includes at least one infrared camera positioned to capture thermal images of the asset, at least one smart mount supporting and electrically coupled to the at least one infrared camera and including a wireless communication module, memory storage, a battery module operative to recharge the at least one infrared camera, an ambient sensor module adapted to obtain ambient condition data and a structural probe sensor to obtain CUI-related data from the asset. At least one computing device has a wireless communication module that communicates with the at least one smart mount and is configured with a machine learning algorithm that outputs a CUI prediction regarding the asset. A cloud computing platform receive and stores the received data and the prediction output and to receive verification data for updating the machine learning algorithm stored on the computing device.

FIELD OF THE INVENTION

The present invention relates to inspection technologies, and, moreparticularly, relates to a cloud-based system for the prediction anddetection of corrosion under insulation (CUI).

BACKGROUND OF THE INVENTION

Corrosion under insulation (CUI) is a condition in which an insulatedstructure such as a metal pipe suffers corrosion on the metal surfacebeneath the insulation. As the corrosion cannot be easily observed dueto the insulation covering, which typically surrounds the entirestructure, CUI is challenging to detect. The typical causes of CUI aremoisture buildup that infiltrates into the insulation material. Watercan accumulate in the annular space between the insulation and the metalsurface, causing surface corrosion. Sources of water that can inducecorrosion include rain, water leaks, and condensation, cooling watertower drift, deluge systems and steam tracing leaks. While corrosionusually begins locally, it can progress at high rates if there arerepetitive thermal cycles or contaminants in the water medium such aschloride or acid.

When CUI is undetected, the results of can lead to the shutdown of aprocess unit or an entire facility and can lead to catastrophicincidents. Since it is a hidden corrosion mechanism, the damage remainsunnoticed until insulation is removed or advanced NDT (non-destructivetesting) techniques, such as infrared thermography, are used toascertain the metal condition beneath the insulation. Removal ofinsulation can be a time-consuming and costly process, while theaccuracy of NDT techniques can be insufficient due to the large numberof variables (e.g., geometrical, environmental, material-related), thatcause false positives (incorrect detection of corrosion) and falsenegatives (incorrect non-detection of corrosion) in the detectionprocess. Additionally, many facilities have elevated networks of pipesthat are difficult to access, requiring scaffolding for visualinspection.

Due to these challenges, it has been found that localized visualinspections of assets are not reliably effective at detecting CUI, andthey do not reflect conditions of the assets. There is a relatedtechnical gap in predictive risk assessment of CUI. Accordingly, thereis a pressing need for improved detection and risk assessment tools todetermine levels of CUI damage, institute proper maintenance scheduling,and reduce the burdensome costs imposed by this problem.

It is with respect to these and other considerations that the disclosuremade herein is presented.

SUMMARY OF THE INVENTION

Embodiments of the present invention provide a system for predicting anddetecting of corrosion under insulation (CUI) in an infrastructureasset. The system includes at least one infrared camera positioned tocapture thermal images of the asset, at least one smart mountmechanically supporting and electrically coupled to the at least oneinfrared camera, the at least one smart mount including a wirelesscommunication module, memory storage adapted to store thermal image datareceived from the at least one camera, a battery module operative torecharge the at least one infrared camera, an ambient sensor moduleadapted to obtain ambient condition data; and a structural probe sensoradapted to obtain CUI-related data from the asset. The system furtherincludes at least one computing device having a wireless communicationmodule that is communicatively coupled to the at least one smart mount,the computing device configured with instructions for executing amachine learning algorithm taking as inputs thermal image data, ambientcondition data and CUI-related data from the probe sensor, andoutputting a CUI prediction regarding the asset, and a cloud computingplatform adapted to receive and store the thermal image data, ambientcondition data and CUI-related data from the probe sensor, and theprediction output by the computing device, the cloud computing platformadapted to receive verification data for updating the machine learningalgorithm stored on the computing device.

In certain embodiments, the at least one smart mount includes a fixturefor supporting the infrared camera, the mount being rotatable andextendable to enable the infrared camera to be translated and tilted.

In certain implementations, asset includes identification tags and atleast one smart mount further includes a standard camera operative toscan the identification tags on the asset.

In certain implementations, the ambient sensor module is operative todetect temperature, humidity and air pressure. The structural probesensor can include a magnetometry sensor.

In certain embodiment, the system further comprises a control stationcommunicatively coupled to the at least one smart mount and adapted totransmit configuration and control commands to the at least one smartmount.

The machine learning algorithm employed by the at least one computingdevice can include a deep recurrent neural network, and in someimplementations, the deep recurrent neural network is a long short-termmemory (LSTM) network. The machine learning algorithm employed by the atleast one computing device can further include a convolutional neuralnetwork.

In some implementations, the at least one computing device is configuredto perform noise reduction on the data received from the at least onesmart mount. The system can have multi-node capability in which each ofthe at least one mounts can communicate with each other via theirrespective communication modules.

Embodiments of the present invention also provide a method of obtainingdata from an infrastructure asset for enabling prediction and detectionof corrosion-under-insulation (CUI). The method comprises capturingthermal image data of the asset over time, probing the asset using anadditional sensing mode to obtain additional probe over time, measuringambient conditions to obtain ambient condition data over time, combiningthe thermal image, additional probe and ambient condition data into acomputer readable file, and transmitting the file to a computing devicethat uses an algorithm that uses the thermal image, additional probe andambient condition data to predict whether the asset contains CUI.

Certain embodiments of the method further comprise scanning the assetfor identification tags to obtain tag photo data and including the tagphoto data in the computer readable file. The additional sensing modecan include, for example, a magnetometry sensor. The ambient conditiondata can include temperature, humidity and air pressure measurements.

Embodiments of the present invention also provide a method of predictingcorrosion-under-insulation (CUI) in an infrastructure asset using acloud computing platform. The method comprises receiving a stream ofdata including thermal images of the asset, additional sensor probe dataof the asset, and ambient conditions at the asset, executing, in realtime, one or more machine learning algorithms using the received streamof data and weights received as updated from the cloud computingplatform to generate a prediction as to whether the asset contains CUI,and transmitting the received stream of data and prediction to the cloudcomputing platform.

Some embodiments of the method further comprise filtering the receiveddata for noise.

The method can also include generating synthetic thermal image databased on ambient conditions and parameters of the asset using a thermaldynamic model. At the cloud computing platform, the synthetic thermalimage data can be combined with the stream of including thermal imagesof the asset, additional sensor probe data of the asset, and ambientconditions at the asset to create a data training set for training amachine learning model. In some embodiments, the machine learning modelincluding a deep recurrent neural network. Implementations of the

can include a long short memory network (LSTM). The machine learningmodel can further include a convolutional neural network.

These and other aspects, features, and advantages can be appreciatedfrom the following description of certain embodiments of the inventionand the accompanying drawing figures and claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic illustration of a cloud-based learning system forpredicting and detecting CUI according to an embodiment of the presentinvention.

FIG. 2 is a schematic illustration of an embodiment of the cloud-basedsystem in which four infrared cameras and corresponding smart mounts andcomputing devices are deployed to monitor a structure for CUI.

FIG. 3 is a block diagram showing functional elements of a smart mountaccording to an exemplary embodiment of the present invention.

FIG. 4 is a block flow diagram illustrating a method for generatingsynthetic thermal image data structures according to an exemplaryembodiment of the present invention.

FIG. 5A is a flow chart of a method for acquiring data for CUIpredication performed using an investigative kit according to anembodiment of the present invention.

FIG. 5B is a flow chart of a method of real time CUI predictionaccording to an embodiment of the present invention.

DETAILED DESCRIPTION OF CERTAIN EMBODIMENTS OF THE INVENTION

Embodiments of the present invention provide a predictive approach fordetecting corrosion under insulation (CUI) taking into account dependentand independent surrounding variables. Thermal images of investigatedassets are captured over time.

As thermal images are captured over time, changes in phenomena can bereadily observed, including the impact of temporary issues such as wind.The thermal images show temperature gradients indicative of locationsvulnerable to CUI. Additional evaluations are performed with anindependent non-destructive testing (NDT) technique, such as, forexample, electromagnetic detection using a magnetometry sensor, todetermine correlative relationships. This “sensor fusion” increases theaccuracy of CUI detection, shadow detection, or abnormal processactivities, the effects of which can be minimized. Ambient conditiondata such as the time of day, weather, process conditions, etc. areincluded as parameter inputs to machine learning algorithms that areused to generate conclusions from the multiple sources of input.Additionally, in some embodiments, to reduce the effects of “noise” inthe thermal images caused by shadows, reflections or other artifacts, anoise filter can be employed as a preprocessing step.

Through the combination of sensor fusion and time-based analysisnon-determinative or confounding variables can be excluded, allowing thelearning algorithms to zero-in on anomalies that are contrary to ambientconditions, and thus are more likely indicative of CUI. Such anomaliesare recorded; afterwards field engineers can perform a verificationinspection upon the locations where such anomalies occur. The results ofthe field inspection (i.e., a “CUI verified” or “CUI not verified”) canbe stored on cloud-based platforms and used to train supervised machinelearning systems, enabling the systems to become more ‘intelligent’ overtime as parameters (weights, factors) are refined over time by acontinually more encompassing data set.

FIG. 1 is a schematic illustration of a cloud-based learning system 100for prediction and detection of CUI according an embodiment of thepresent invention. FIG. 1 shows an exemplary structure 105 to be tested,in this case a set of insulated pipes. The insulated pipes of thisexample can comprise a metallic pipe conduit surrounded by one or morelayers of insulation. Corrosion, when it occurs, tends form in theannular region between the insulation and the metallic pipe wheremoisture can become trapped and accumulate. In FIG. 1, one or moreinfrared cameras 110 (only one camera is shown in the figure) aresituated proximally to the structure 105 to capture infrared radiationand record thermal images emitted from the structure. One example of asuitable infrared camera for CUI detection is the C3 Wi-Fi enabledthermal camera supplied by FLIR Systems, Inc. of Wilsonville, Oreg.,although other devices can also be used. The thermal images capturedfrom the structure 105 reveal internal thermal contrasts within thestructure that are undetectable in the visible spectrum radiation andcan be indicative of moisture accumulation and/or corrosion. Theinfrared camera 110 preferably captures thermal images received fromregions of the structure continuously over a selected duration, and/orintermittently at different times or dates. The camera 110 is adapted toconvert the thermal images into a standardized computer-readable fileformat (i.e., thermograph files, jpgs).

The infrared camera 110 is positioned on a mount 112, which as describedin greater detail below, can be “smart” and have a variety of componentsand functions. In some embodiments, the mount can be implemented as atripod. The mount 112 can be extendable to reach high elevations on thestructure (e.g., by telescoping) and can include a mechanical headfixture coupling to the camera that has several degrees of freedom topan and tilt at various angles with respect to a fixed plane. Fieldtechnical personal can set the extension and orientation of the mounthead to capture thermal images from different areas of the structure, asrequired.

In some facilities, identification tags can be posted on assets, orportions thereof. The precise geographical location of each tag can bedetermined using GPS. The identification tags can be implemented usingimage-based tags such as QR codes that are readable from a distance. Totake advantage of the tagging feature, in some embodiments, a standardcamera can be included along with the infrared camera on the mount toscan tags on the assets. Depending on the size of tags (of known size)in the image, distances from the camera to the tags can be determined.Tagging enables simultaneous scanning and localization of the facilityassets without the need to create complex three-dimensional CAD modelsof the facility.

The infrared camera 110 can be physically and communicatively coupled tothe mount 112 (e.g., wirelessly by Bluetooth or Wi-Fi communication).The mount 112 also includes or is coupled to one or more additionaldetectors, such as electromagnetic sensors (not shown in FIG. 1), whichcan be used to probe the structure and obtain supplemental readings tocomplement the data obtained by thermal imaging. In this manner, datafrom two or more distinct and independent sensing modes can be combined,referred to as “sensor fusion”, that can make downstream prediction anddetection much more robust by reduction of false positiveclassifications. The mount 112 also includes sensors for detectingambient conditions including temperature, humidity, and air pressure.Received thermal images can be associated with the ambient conditionsand the current time at which the ambient conditions are recorded. Thisdata comprises parameters used by the machine learning algorithms thatcontribute to the interpretation and classification of the thermalimages captured from the structure.

The mount 112 is communicatively coupled to a computing device 115,which can be a tablet, laptop or any other suitable computing devicewith sufficient processing and memory capability that can beconveniently taken onsite in the field for use by field technicalprofessionals. The mount 112 is operative to transmit thermographicfiles received from the camera 110 to the computing device 115. Thecomputing device 115 preferably stores executable applications forpreprocessing and predictive analysis. Preprocessing can include imagefiltering steps for reducing noise in the images that can arise frommany causes. The computer device also executes one or more machinelearning algorithms that take the received thermograph files (thermalimages) as inputs and output a prediction as to the probability that thethermal images contain anomalies of interest in real time. As discussedin related commonly-owned application, U.S. patent application Ser. No.15/712,490, entitled “Thermography Image Processing with Neural Networksto Identify Corrosion Under Insulation (CUI)”, a plurality of machinelearning algorithms, including deep learning algorithms can be used forCUI detection. In some implementations, convolutional networks, whichare useful for classifying images in detail, are used in a first stage,and recurrent neural networks, which are useful for tracking changesover time, are used in an additional stage. The computing device 115provides the output of the machine learning algorithms in an applicationuser interface that can be conveniently consulted by field technicalpersonnel. Real time predicative analysis in the field allows fieldtechnical personal to support observations and focus rapidly onhigh-risk areas of the structure that are more likely subject tocorrosion damage.

The computing device 115 communicates wirelessly via a network switch120 (via wireless communication network 122) with a cloud computingplatform 125. Wireless network 122 can be a wireless local area network(WLAN), wireless wide area networks (WWAN), cellular networks or acombination of such networks. The cloud computing platform 125 comprisescomputing resources, typically dynamically allocated, including one ormore processors (e.g., one or more servers or server clusters), that canoperate independently or collaboratively in a distributed computingconfiguration. The cloud computing platform 125 includes databasestorage capacity for storing computer-executable instructions forhosting applications and for archiving received data for long termstorage. For example, computing device 115 in the field can upload allthermal image and other data received to the cloud computing platform125 for secure storage and for further processing and analysis. Morespecifically, the computing device 115 can format and send data recordsin, for example, MySQL or another database format. An example databaserecord can include, among other fields, a tagged asset location, aseries of thermal images taken over time at a particular asset location(or a link thereto), the data value for the camera's ID (cameraID) ofthe camera that captured the thermal images, the time/date at which eachimage was captured, ambient conditions at the time/date (e.g.,temperature), sensor fusion data (e.g., electromagnetic sensor data).The cloud database can store include a detailed geographical mapping ofthe location and layout of the infrastructure assets (e.g., from LiDARdata) and applications executed on the cloud platform can performdetailed analyses that combine the sensor data and predictive analyseswith the detailed mapping of the assets to make risk assessmentscovering entire structures or groups of structures. Reports of suchassessments and results of other processing performed at the cloudcomputing platform 125 are accessible to a control station 130communicatively coupled to the cloud computing platform. In alternativeembodiments, it is possible for the smart mount 112 to format andtransmit the received data to the cloud computing platform directlybefore analysis of the data is performed on site.

FIG. 2 depicts an exemplary implementation of a cloud-based learningsystem for CUI prediction and detection more generally shown in FIG. 1.In FIG. 2, this system 150 includes four sets of cameras, mounts andcomputing devices (“investigative kits”) positioned at various positionsin proximity to structure 105 for capturing thermal image and otherdata. Although four investigative kits are used in this embodiment, itis again noted that fewer or a greater number of kits can be employeddepending, for example, on the size of the structure or installationinvestigated. More specifically, the system 150 is configured using afirst infrared camera 152 associated with a first mount 154 and firstcomputing device 156 positioned at a first location; a second infraredcamera 162 associated with a second mount 164 and second computingdevice 166 positioned at a second location; a third infrared camera 172associated with a third mount 174 and third computing device 176positioned at a third location; and a fourth infrared camera 182associated with a fourth mount 184 and fourth computing device 186positioned at a forth location proximal to the asset 105. Two-waywireless communications can be supported by all the mounts and computingdevices of the system, each of which can thus communicate with eachother. For example, thermal image data received by the computing devices156, 166, 176, 186, can be transmitted to the cloud computing platform125 via network switch 120, and to control station 130. Alternatively,the smart mounts 154, 164, 174, 184 can communicate directly with thecontrol station when wireless connectivity is available. By providingredundant connectivity, each smart mount or computing device in thesystem can act as a communication node in a multi-node system, so thatif one or more of the mounts or computing devices loses connectivitywith the control station, data can be forwarded to other nodes thatmaintain connectivity. The control station 130 is configured to provideconfiguration and control commands to the smart mounts 154, 164, 174,184 or computing devices 156, 166, 176, 186.

FIG. 3 is a block diagram showing functional elements of a smart mountaccording to an exemplary embodiment of the present invention. The smartmount 112 includes a camera coupling or mount 202 by means of which theinfrared camera 110 can be securely mechanical affixed and electricallyconnected to the mount 112. As noted above, the camera coupling 202 caninclude expandable and rotatable elements, such as telescoping shafts,and various joints with degrees of freedom for enabling the camera to betranslated and tilted to a desired position and orientation. In someimplementations, the smart mount can be supported on a counterweightedmovable to provide a steering sub-system on the ground.

To enable inter-communication with other elements of the system, thesmart mount 112 also includes a communication module 204 which caninclude an antenna, a transceiver, and electronic components configuredto support two-way wireless communication with other smart mounts,computing devices, and the control station 130. The smart mount alsoincludes a memory module 206 which can be implemented using SSD cardmemory. If the infrared cameras are mounted in locations where signalobstructions result in suboptimal data rates that are inferior to theactual thermal image streaming rate, the onboard memory module can beused to store the thermal image stream to provide latency while thewireless attempts to support the data download.

The smart mount 112 further includes an ambient sensor module 210 thatcan include temperature, humidity and pressure sensors. An additionalstructural probe sensor module 212 includes detectors that can be usedto probe the structure for CUI using modes distinct from thermalimaging, including, without limitation, magnetic (magnetometry) andultrasonic detectors. Together with the thermal images from the infraredcamera, the structural probe sensor module provides the sensor fusionthat enhances CUI prediction and risk assessment. An electrical powermodule 220 includes a battery module 222 of sufficient size to provideelectrical power for the smart mount components and to charge theinfrared camera battery via a power supply circuit 224 for a suitabledata gathering period before requiring recharging. A suitable durationfor data gathering can be for example, about 45 minutes to about 90minutes. Larger or smaller batteries can be employed for longer orshorter data gathering periods.

In operation, the field computing devices receive (ingest) thermalimage, probe sensor and ambient condition data from the infrared camerasand smart mounts. The initial data ingest can be affected by conditionsat the site, including, shadows, reflections and spurious signals.Before executing machine learning algorithms, it can be useful to filterincoming data for noise using noise filtering mechanism integratedwithin the software as a preprocessing step to filter out noise andamplify the signal-to-noise ratio. In some embodiments, ingested datacan be filtered by dimensionality reduction and autoencoding techniques.In other embodiments, linear or non-linear smoothing filters can beapplied instead of or in addition to dimensionality reductiontechniques. The noise filtering step helps discriminate CUI signals fromshadows, reflections as well as normal near infrared thermal signals.While such noise and other artifacts in the data can be eventuallyrecognized and compensated for in the machine learning process usingmulti-context embedding in the neural network stage, it can be more timeand resource efficient to preprocess the data by filtering in thismanner.

Another refinement which can be used to enhance robustness to noise, isthe introduction of synthetic training data to supplement data takenfrom the field. Mathematical models including finite element analysesare based on the thermal dynamics of insulated metal structures and onthermal images taken in the field as a basis for calibration andcomparison. The synthetic data can be to simulate and augment thethermal image training dataset. The synthetic data can also make thelearning system more robust to different environmental conditions suchas weather conditions, temperature, exposure to sun light, and materialtemperature behind the insulation, for example. The synthetic data canbe generated locally by the computing devices or the cloud computingplatform. In either case the synthetic data can incorporated in thetraining and application database at the cloud computing platform.

FIG. 4 is a block flow diagram illustrating a method for generatingsynthetic thermal image data structures according to the presentinvention for supplementing a training set for a predictive machinelearning model. The inputs for generating synthetic thermal imagesinclude environmental variables 302 (e.g., temperature, humidity, airpressure, time of day), asset parameters 304 (e.g., dimensions,position, material, insulation), and a set of thermal images 306 ofvarious assets captured in the field (“field thermographs”). Theenvironmental variables 302 and asset parameters 304 are input to athermal dynamics model 310 that uses known thermodynamic properties ofmaterials based on environmental conditions to generate a synthetictemperature map 315 of insulated assets over time, based on a randomprobability distribution of temperature and humidity conditions. Thesynthetic temperature map 315 and the field thermographs are inputs toan imaging model 320. While images can be created from the temperaturemap alone, the field thermographs can be used as a basis of calibrationand comparison. As an example, if a temperature maps of assets exhibitsa tendency toward greater temperature contrasts than shown in fieldthermographs of similar asset under similar conditions, the imagingmodel can make weighting adjustments to bring the temperature map closerto the field thermographs. After such adjustments are made, the imagingmodel generates a set of synthetic thermal images 325 that can be usedto supplement the field thermographs during training.

FIG. 5A is a flow chart of a method for acquiring data for CUIpredication performed using an investigative kit according to anembodiment of the present invention. The method begins in step 400. Instep 402, smart mounts and cameras (infrared, standard) are installed atsuitable locations to monitor assets at a facility. In step 404, anytags posted on the assets are scanned. In step 406, thermal image,sensor fusion, and ambient condition data are captured and stored inmemory. In step 408, this information is transmitted to a localcomputing device for real time analysis. The method ends in step 410.

FIG. 5B is a flow chart of a method of real time CUI predictionaccording to an embodiment of the present invention. In step 500 themethod begins. In step 502, the computing device receives the captureddata from the smart mounts. In step 504, the received data is filteredfor noise. In step 506, CUI prediction and detection is conducted usingmachine learning algorithms based on the filtered data and parameterweights from prior training. The machine learning algorithms can includedeep learning techniques such as convolutional and recurrent neuralnetworks. In an optional step 508, synthetic data is generated tosupplement the data received from the smart mounts. In step 510,prediction output is generated on a graphical user interface to beviewed by field technical personnel. In a following step 512, thereceived data and the prediction output is transmitted to the cloudcomputing platform. In step 514, the method ends.

It is to be understood that any structural and functional detailsdisclosed herein are not to be interpreted as limiting the systems andmethods, but rather are provided as a representative embodiment and/orarrangement for teaching one skilled in the art one or more ways toimplement the methods.

It is to be further understood that like numerals in the drawingsrepresent like elements through the several figures, and that not allcomponents and/or steps described and illustrated with reference to thefigures are required for all embodiments or arrangements

The terminology used herein is for describing particular embodimentsonly and is not intended to be limiting of the invention. As usedherein, the singular forms “a”, “an” and “the” are intended to includethe plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “comprises”and/or “comprising”, when used in this specification, specify thepresence of stated features, integers, steps, operations, elements,and/or components, but do not preclude the presence or addition of oneor more other features, integers, steps, operations, elements,components, and/or groups thereof.

Terms of orientation are used herein merely for purposes of conventionand referencing and are not to be construed as limiting. However, it isrecognized these terms could be used with reference to a viewer.Accordingly, no limitations are implied or to be inferred.

Also, the phraseology and terminology used herein is for the purpose ofdescription and should not be regarded as limiting. The use of“including,” “comprising,” or “having,” “containing,” “involving,” andvariations thereof herein, is meant to encompass the items listedthereafter and equivalents thereof as well as additional items.

While the invention has been described with reference to exemplaryembodiments, it will be understood by those skilled in the art thatvarious changes can be made and equivalents can be substituted forelements thereof without departing from the scope of the invention. Inaddition, many modifications will be appreciated by those skilled in theart to adapt a particular instrument, situation or material to theteachings of the invention without departing from the essential scopethereof. Therefore, it is intended that the invention not be limited tothe particular embodiment disclosed as the best mode contemplated forcarrying out this invention, but that the invention will include allembodiments falling within the scope of the appended claims.

What is claimed is:
 1. A system for predicting and detecting ofcorrosion under insulation (CUI) in an infrastructure asset usingmachine learning and data fusion comprising: at least one infraredcamera positioned to capture thermal images of the asset; at least onesmart mount mechanically supporting and electrically coupled to the atleast one infrared camera, the at least one smart mount including: awireless communication module; memory storage adapted to store thermalimage data received from the at least one camera; a battery moduleoperative to recharge the at least one infrared camera; an ambientsensor module adapted to obtain ambient condition data; and a structuralprobe sensor adapted to obtain CUI-related data from the asset; at leastone computing device having a wireless communication module that iscommunicatively coupled to the at least one smart mount, the computingdevice being configured with instructions for executing a machinelearning algorithm taking as inputs thermal image data, ambientcondition data and CUI-related data from the probe sensor providing datafusion, and adapted to output a CUI prediction regarding the asset; anda cloud computing platform adapted to receive and store the thermalimage data, ambient condition data and CUI-related data from the probesensor, and the prediction output by the computing device, the cloudcomputing platform adapted to receive verification data for updating themachine learning algorithm stored on the computing device.
 2. The systemof claim 1, wherein the at least one smart mount includes a fixture forsupporting the infrared camera, the fixture being rotatable andextendable to enable the infrared camera to be translated and tilted. 3.The system of claim 1, wherein the asset includes identification tagsand at least one smart mount further includes a standard cameraoperative to scan the identification tags on the asset.
 4. The system ofclaim 1, wherein the ambient sensor module is operative to detecttemperature, humidity and air pressure.
 5. The system of claim 1,wherein the structural probe sensor includes a magnetic sensor.
 6. Thesystem of claim 1, further comprising a control station communicativelycoupled to the at least one smart mount and adapted to transmitconfiguration and control commands to the at least one smart mount. 7.The system of claim 1, wherein the machine learning algorithm employedby the at least one computing device includes a deep recurrent neuralnetwork.
 8. The system of claim 7, wherein the deep recurrent neuralnetwork is a long short term memory (LSTM) network.
 9. The system ofclaim 1, wherein the machine learning algorithm employed by the at leastone computing device further includes a convolutional neural network.10. The system of claim 1, wherein the at least one computing device isconfigured to perform noise reduction on the data received from the atleast one smart mount.
 11. The system of claim 1, wherein each of the atleast one mounts can communicate with each other via their respectivecommunication modules.
 12. The system of claim 1, wherein the at leastone smart mount is implemented as a tripod.
 13. A method of obtainingdata from an infrastructure asset for enabling prediction and detectionof corrosion-under-insulation (CUI) comprising: capturing thermal imagedata of the asset overtime; probing the asset using an additionalsensing mode to obtain additional probe over time; measuring ambientconditions to obtain ambient condition data overtime; combining thethermal image, additional probe and ambient condition data into acomputer readable file; and scanning the asset for identification tagsto obtain tag photo data: including the tag photo data in the computerreadable file: and transmitting the file to a computing device that usesan algorithm that uses the thermal image, additional probe and ambientcondition data to predict whether the asset contains CUI.
 14. The methodof claim 12, wherein the additional sensing mode is based onmagnetometry.
 15. The method of claim 12, wherein the ambient conditiondata includes temperature, humidity and air pressure measurements.
 16. Amethod of predicting corrosion-under-insulation (CUI) in aninfrastructure asset using a cloud computing platform and data fusion,comprising: receiving a stream of data including thermal images of theasset, additional sensor probe data of the asset, and ambient conditionsat the asset; executing, in real time, one or more machine learningalgorithms using the received stream of data and weights received asupdated from the cloud computing platform to generate a prediction as towhether the asset contains CUI; and transmitting the received stream ofdata and prediction to the cloud computing platform.
 17. The method ofclaim 15, further comprising filtering the received data for noise. 18.The method of claim 15, further comprising generating synthetic thermalimage data based on ambient conditions and parameters of the asset usinga thermal dynamic model.
 19. The method of claim 17, wherein, at thecloud computing platform, the synthetic thermal image data is combinedwith the stream of including thermal images of the asset, additionalsensor probe data of the asset, and ambient conditions at the asset tocreate a data training set for training a machine learning model. 20.The method of claim 18, wherein the machine learning model including adeep recurrent neural network.
 21. The method of claim 19, wherein thedeep recurrent neural network includes a long short memory network(LSTM).
 22. The method of claim 18, wherein the machine learning modelfurther includes a convolutional neural network.